Krahasoni metodat

	Filtri Dinamik i Grimcave	Filtri i grimcave (Monte Karlo Sekuencial)
Fusha	Statistika bajesiane	Statistika bajesiane
Familja	Bayesian methods	Bayesian methods
Viti i origjinës	1993	1993
Krijuesi	Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001)	Gordon, Salmond & Smith
Lloji	Sequential Bayesian state estimation	Sequential Monte Carlo estimator
Burimi themelues	Doucet, A., de Freitas, N. & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer. ISBN: 978-0387951461	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.
Emërtime të tjera	dynamic sequential Monte Carlo, dynamic SMC, bootstrap particle filter, dynamic SIR filter	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Të lidhura	4	4
Përmbledhja	A dynamic particle filter is a sequential Monte Carlo algorithm that tracks an evolving hidden state over time by maintaining a population of weighted random samples — particles — each representing a plausible trajectory. As new observations arrive, particle weights are updated via the likelihood and the population is resampled, keeping the representation concentrated on the most probable state regions in a fully nonlinear and non-Gaussian setting.	The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.